pugsql VS powderkeg

I've had good experience using PugSQL to write SQL in Python [1]. You write the SQL inside SQL files so you can actually benefit from syntax highlighting, static analysis, etc. At the different of writing strings of SQL inside Python files.

[1] https://pugsql.org

There's a tool called PugSQL that looks promising for Python, but it seems that async isn't directly supported yet[0]. If I ever find time, I'd love to jump on this and make it work, but nobody should hold their breath for that.

[0] https://github.com/mcfunley/pugsql/issues/44

As a data engineer, the pattern the OP shares is very familiar. I find it much preferable to use of ORMs for wide variety of reasons. However, I view implementing with SQL as an antiquated problem rather than a pragmatic feature. The evolution of this pattern would be to integrate database querying into languages more directly and eliminate SQL entirely. While this could be achieved in Python, I find that a language like Clojure, via functional programming (FP) primitives and transducers, is a natural candidate, particularly for JVM implemented databases. Rather than encapsulating SQL via query building or ORM based APIs, an FP core could be integrated into database engines to allow, via transducers, complex native forms to be executed directly across database clusters. Apache Spark is an analog of this. In particular the Clojure project, powderkeg (https://github.com/HCADatalab/powderkeg) demonstrates the potential of utilizing transducers in a database cluster context.